scholarly journals Simulating and Forecasting the Cumulative Confirmed Cases of SARS-CoV-2 in China by Boltzmann Function-based Regression Analyses

2020 ◽  
Author(s):  
Xinmiao Fu ◽  
Qi Ying ◽  
Tieyong Zeng ◽  
Tao Long ◽  
Yan Wang
Keyword(s):  
Confidence Interval ◽  
Mainland China ◽  
Hubei Province ◽  
Atypical Pneumonia ◽  
Cumulative Number ◽  
Regression Analyses ◽  
Wuhan City ◽  
Boltzmann Function ◽  
Geographic Regions ◽  
Novel Coronavirus

AbstractAn ongoing outbreak of atypical pneumonia caused by the 2019 novel coronavirus (SARS-CoV-2) is hitting Wuhan City and has spread to other provinces/cities of China and overseas. It very urgent to forecast the future course of the outbreak. Here, we provide an estimate of the potential total number of confirmed cases in mainland China by applying Boltzmann-function based regression analyses. We found that the cumulative number of confirmed cases from Jan 21 to Feb 14, 2020 for mainland China, Hubei Province, Wuhan City and other provinces were all well fitted with the Boltzmann function (R2 being close to 0.999). The potential total number of confirmed cases in the above geographic regions were estimated at 95% confidence interval (CI) as 79589 (71576, 93855), 64817 (58223, 77895), 46562 (40812, 57678) and 13956 (12748, 16092), respectively. Notably, our results suggest that the number of daily new confirmed cases of SARS-CoV-2 in mainland China (including Hubei Province) will become minimal between Feb 28 and Mar 10, 2020, with 95% CI. In addition, we found that the data of cumulative confirmed cases of 2003 SARS-CoV in China and Worldwide were also well fitted to the Boltzmann function. To our knowledge this is the first study revealing that the Boltzmann function is suitable to simulate epidemics. The estimated potential total number of confirmed cases and key dates for the SARS-CoV-2 outbreak may provide certain guidance for governments, organizations and citizens to optimize preparedness and response efforts.

scholarly journals Forecasting the cumulative number of COVID-19 deaths in China: a Boltzmann function-based modeling study

2020 ◽  
Vol 41 (7) ◽  
pp. 841-843 ◽  
Author(s):  
Yuanyuan Gao ◽  
Zuqin Zhang ◽  
Wei Yao ◽  
Qi Ying ◽  
Cheng Long ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Hubei Province ◽  
Cumulative Number ◽  
Wuhan City ◽  
Boltzmann Function ◽  
Modeling Study

AbstractThe COVID-19 outbreak is ongoing in China. Here, Boltzmann function-based analyses reveal the potential total numbers of COVID-19 deaths: 3,260 (95% confidence interval [CI], 3187–3394) in China; 110 (95% CI, 109–112) in Hubei Province; 3,174 (95% CI, 3095–3270) outside Hubei; 2,550 (95% CI, 2494–2621) in Wuhan City; and 617 (95% CI, 607–632) outside Wuhan.

scholarly journals Forecasting the Cumulative Number of COVID-19 Deaths in China: a Boltzmann Function-based Modeling Study

2020 ◽  
Author(s):  
Yuanyuan Gao ◽  
Zhongyan Li ◽  
Qi Ying ◽  
Cheng Long ◽  
Xinmiao Fu
Keyword(s):  
Hubei Province ◽  
Cumulative Number ◽  
Wuhan City ◽  
Boltzmann Function ◽  
Modeling Study

AbstractThe COVID-19 outbreak is on-going in China. Here we estimated the potential total numbers of COVID-19 deaths in China, outside Hubei (in China), Hubei Province, Wuhan City and outside Wuhan (in Hubei) by Boltzmann function-based analyses, which are 3342 (95% CI, 3214, 3527), 111 (109, 114), 3245 (3100, 3423), 2613 (2498, 2767) and 627 (603, 654), respectively. The results may help to evaluate the severity of COVID-19 outbreaks and facilitate timely mental service for the families of passed patients.

scholarly journals Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study

2020 ◽  
Author(s):  
Juanjuan Zhang ◽  
Maria Litvinova ◽  
Wei Wang ◽  
Yan Wang ◽  
Xiaowei Deng ◽  
...  
Keyword(s):  
Age Groups ◽  
Mainland China ◽  
The Elderly ◽  
Hubei Province ◽  
Epidemic Threshold ◽  
Epidemic Dynamics ◽  
Serial Interval ◽  
The Mean ◽  
Novel Coronavirus ◽  
Local Transmission

AbstractBackgroundThe COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.MethodsWe collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.ResultsThe median age of the cases was 44 years, with an increasing of cases in younger age groups and the elderly as the epidemic progressed. The delay from symptom onset to hospital admission decreased from 4.4 days (95%CI: 0.0-14.0) until January 27 to 2.6 days (0.0-9.0) from January 28 to February 17. The mean incubation period was estimated at 5.2 days (1.8-12.4) and the mean serial interval at 5.1 days (1.3-11.6). The epidemic dynamics in provinces outside Hubei was highly variable, but consistently included a mix of case importations and local transmission. We estimate that the epidemic was self-sustained for less than three weeks with Rt reaching peaks between 1.40 (1.04-1.85) in Shenzhen City of Guangdong Province and 2.17 (1.69-2.76) in Shandong Province. In all the analyzed locations (n=10) Rt was estimated to be below the epidemic threshold since the end of January.ConclusionOur findings suggest that the strict containment measures and movement restrictions in place may contribute to the interruption of local COVID-19 transmission outside Hubei Province. The shorter serial interval estimated here implies that transmissibility is not as high as initial estimates suggested.

scholarly journals COVID-19 in a Designated Infectious Diseases HospitalOutside Hubei Province,China

2020 ◽  
Author(s):  
Qingxian Cai ◽  
Deliang Huang ◽  
Pengcheng Ou ◽  
Hong Yu ◽  
Zhibin Zhu ◽  
...  
Keyword(s):  
Hubei Province ◽  
Initial Symptom ◽  
Wuhan City ◽  
Hospital System ◽  
Risk Of Progression ◽  
New Type ◽  
Coronavirus Infection ◽  
Novel Coronavirus ◽  
Underlying Diseases ◽  
Overall Mortality

AbstractBackgroundA new type of novel coronavirus infection (COVID-19) occurred in Wuhan, Hubei Province. Previous investigations reported patients in Wuhan city often progressed into severe or critical and had a high mortality rate.The clinical characteristics of affected patients outside the epicenter of Hubei province are less well understood.MethodsAll confirmed COVID-19 case treated in the Third People’s Hospital of Shenzhen,from January 11, 2020 to February 6, 2020, were included in this study. We analyzed the epidemiological and clinical features of these cases to better inform patient management in normal hospital settings.ResultsAmong the 298 confirmed cases, 233(81.5%) had been to Hubei while 42(14%) had not clear epidemiological history. Only 192(64%) cases presented with fever as initial symptom. The lymphocyte count decreased in 38% patients after admission. The number (percent) of cases classified as non-severe and severe was 240(80.6%) and 58(19.4%) respectively. Thirty-two patients (10.7%) needed ICU care. Compared to the non-severe cases, severe cases were associated with older age, underlying diseases, as well as higher levels of CRP, IL-6 and ESR. The median (IRQ) duration of positive viral test were 14(10-19). Slower clearance of virus was associated with higher risk of progression to severe clinical condition. As of February 14, 2020, 66(22.1%) patients were discharged and the overall mortality rate remains 0.ConclusionsIn a designated hospital outside the Hubei Province, COVID-19 patients were mainly characterized by mild symptoms and could be effectively manage by properly using the existing hospital system.

scholarly journals Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China

2020 ◽  
Vol 9 (2) ◽  
pp. 571 ◽  
Author(s):  
Péter Boldog ◽  
Tamás Tekeli ◽  
Zsolt Vizi ◽  
Attila Dénes ◽  
Ferenc A. Bartha ◽  
...  
Keyword(s):  
Control Measure ◽  
Mainland China ◽  
Destination Country ◽  
Control Measures ◽  
Cumulative Number ◽  
Travel Restrictions ◽  
Imported Cases ◽  
Major Outbreak ◽  
Novel Coronavirus ◽  
Local Reproduction

We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies, the effect of travel restrictions, and the efficacy of entry screening at destination; and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number R loc ). We found that in countries with low connectivity to China but with relatively high R loc , the most beneficial control measure to reduce the risk of outbreaks is a further reduction in their importation number either by entry screening or travel restrictions. Countries with high connectivity but low R loc benefit the most from policies that further reduce R loc . Countries in the middle should consider a combination of such policies. Risk assessments were illustrated for selected groups of countries from America, Asia, and Europe. We investigated how their risks depend on those parameters, and how the risk is increasing in time as the number of cases in China is growing.

scholarly journals A comparison of COVID-19, SARS and MERS

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9725 ◽  
Author(s):  
Tingting Hu ◽  
Ying Liu ◽  
Mingyi Zhao ◽  
Quan Zhuang ◽  
Linyong Xu ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Case Fatality ◽  
Hubei Province ◽  
World Health ◽  
Atypical Pneumonia ◽  
Organization Studies ◽  
Optimal Method ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization

In mid-December 2019, a novel atypical pneumonia broke out in Wuhan, Hubei Province, China and was caused by a newly identified coronavirus, initially termed 2019 Novel Coronavirus and subsequently severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of 19 May 2020, a total of 4,731,458 individuals were reported as infected with SARS-CoV-2 among 213 countries, areas or territories with recorded cases, and the overall case-fatality rate was 6.6% (316,169 deaths among 4,731,458 recorded cases), according to the World Health Organization. Studies have shown that SARS-CoV-2 is notably similar to (severe acute respiratory syndrome coronavirus) SARS-CoV that emerged in 2002–2003 and Middle East respiratory syndrome coronavirus (MERS-CoV) that spread during 2012, and these viruses all contributed to global pandemics. The ability of SARS-CoV-2 to rapidly spread a pneumonia-like disease from Hubei Province, China, throughout the world has provoked widespread concern. The main symptoms of coronavirus disease 2019 (COVID-19) include fever, cough, myalgia, fatigue and lower respiratory signs. At present, nucleic acid tests are widely recommended as the optimal method for detecting SARS-CoV-2. However, obstacles remain, including the global shortage of testing kits and the presentation of false negatives. Experts suggest that almost everyone in China is susceptible to SARS-CoV-2 infection, and to date, there are no effective treatments. In light of the references published, this review demonstrates the biological features, spread, diagnosis and treatment of SARS-CoV-2 as a whole and aims to analyse the similarities and differences among SARS-CoV-2, SARS-CoV and MERS-CoV to provide new ideas and suggestions for prevention, diagnosis and clinical treatment.

scholarly journals COVID-19: Diagnosis, Management and Response

2021 ◽  
pp. 161-170
Author(s):  
Ashwini Shalikrao Mhaske ◽  
Swaroopa Chakole
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Hubei Province ◽  
Positive Patient ◽  
The Novel ◽  
Wuhan City ◽  
Corona Virus ◽  
Early Reaction ◽  
Novel Coronavirus ◽  
Predetermined Number

Background: COVID infection 2019 (COVID-19) is identified as a disease caused by Corona virus formally known as severe acute respiratory syndrome (SARS-CoV-2), which was first detected in Wuhan City, Hubei Province, China, amidst of an outbreak of respiratory sickness cases. Summary: As there is no affirmed total course of treatment for the Novel Coronavirus, the best way to handle it is by playing it safe, its administration and early reaction. Segregation and disinfection go inseparably with regards to dealing with a COVID positive patient. In any case, the predetermined number of clinical office accessible is blocking the cycle of control and anticipation for a particularly number of infected patients. Conclusion: As the pandemic is advancing, more examinations and exploration is needed to effectively deal with the spread of the novel Corona virus. Foundation improvement and arrangement of clinical office and gear is the preeminent prerequisite for early reaction and treatment.

scholarly journals Epidemiological characteristics of COVID-19 in medical staff members of neurosurgery departments in Hubei province: A multicentre descriptive study

2020 ◽  
Author(s):  
Qiangping Wang ◽  
Xing Huang ◽  
Yansen Bai ◽  
Xuan Wang ◽  
Haijun Wang ◽  
...  
Keyword(s):  
Relative Risk ◽  
Confidence Interval ◽  
Medical Staff ◽  
Epidemiological Data ◽  
Hubei Province ◽  
Staff Members ◽  
Source Of Infection ◽  
Novel Coronavirus ◽  
Epidemiological Characteristics ◽  
Level 2

ABSTRACTBackgroundThe novel coronavirus (SARS-CoV-2) has infected a large number of healthcare workers in Hubei province, China. In addition to infectious and respiratory disease physicians, many doctors in other medical fields have been infected.MethodsWe prospectively collected epidemiological data on medical staff members who are working in neurosurgery departments in 107 hospitals in Hubei province through self-reported questionnaires or telephone interviews. Data of medical staff members with laboratory-confirmed coronavirus disease 2019 (COVID-19) were analysed. The final follow-up date was 1 March 2020.FindingsA total of 5,442 neurosurgery department medical staff members were surveyed. One hundred and twenty cases, involving 54 doctors and 66 nurses, were found to have been infected with SARS-CoV-2. The overall incidence was 2.2%. These cases were concentrated in 26 centres, 16 of which had admitted a total of 59 patients with COVID-19 complicated by craniocerebral disease. Medical staff members in centres receiving COVID-19 patients had a higher risk of contracting infection than those in centres not receiving COVID-19 patients (relative risk: 19.6; 95% confidence interval: 12.6–30.6). Contact with either COVID-19 patients (62.5%, 75/120) or infected colleagues (30.8%, 37/120) was the most common mode of transmission. About 78.3% (94/120) of the infected cases wore surgical masks, whereas 20.8% (25/120) failed to use protection when exposed to the source of infection. Severe infections were observed in 11.7% (14/120) of the cases, with one death (0.8%, 1/120). All the infected medical staff members had been discharged from the hospital. A total of 1,287 medical staff members were dispatched to participate in the frontline response to COVID-19 under level 2 protection of whom one was infected. Medical staff members who took inadequate protection had a higher risk of contracting infection than those using level 2 protection (relative risk: 36.9; 95% confidence interval: 5.2–263.6).ConclusionsNeurosurgical staff members in Hubei province were seriously affected by COVID-19. Level 2 protection and strengthening of protective measures are likely to be effective in preventing medical workers from being infected.

scholarly journals Estimation of local novel coronavirus (COVID-19) cases in Wuhan, China from off-site reported cases and population flow data from different sources

2020 ◽  
Author(s):  
Zian Zhuang ◽  
Peihua Cao ◽  
Shi Zhao ◽  
Yijun Lou ◽  
Shu Yang ◽  
...  
Keyword(s):  
Likelihood Estimation ◽  
Hubei Province ◽  
Flow Data ◽  
Daily Newspaper ◽  
Wuhan City ◽  
Spring Festival ◽  
Log Likelihood ◽  
Novel Coronavirus ◽  
Different Sources

AbstractBackgroundsIn December 2019, a novel coronavirus (COVID-19) pneumonia hit Wuhan, Hubei Province, China and spread to the rest of China and overseas. The emergence of this virus coincided with the Spring Festival Travel Rush in China. It is possible to estimate total number of cases of COVID-19 in Wuhan, by 23 January 2020, given the cases reported in other cities and population flow data between cities.MethodsWe built a model to estimate the total number of cases in Wuhan by 23 January 2020, based on the number of cases detected outside Wuhan city in China, with the assumption that if the same screening effort used in other cities applied in Wuhan. We employed population flow data from different sources between Wuhan and other cities/regions by 23 January 2020. The number of total cases was determined by the maximum log likelihood estimation.FindingsFrom overall cities/regions data, we predicted 1326 (95% CI: 1177, 1484), 1151 (95% CI: 1018, 1292) and 5277 (95% CI: 4732, 5859) as total cases in Wuhan by 23 January 2020, based on different source of data from Changjiang Daily newspaper, Tencent, and Baidu. From separate cities/regions data, we estimated 1059 (95% CI: 918, 1209), 5214 (95% CI: 4659, 5808) as total cases in Wuhan in Wuhan by 23 January 2020, based on different sources of population flow data from Tencent and Baidu.ConclusionSources of population follow data and methods impact the estimates of local cases in Wuhan before city lock down.

scholarly journals Study on SARS-CoV-2 transmission and the effects of control measures in China

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242649
Author(s):  
Bo Zhang ◽  
Hongwei Zhou ◽  
Fang Zhou
Keyword(s):  
Confidence Interval ◽  
Turning Point ◽  
Estimation Method ◽  
Hubei Province ◽  
Control Measures ◽  
Reproductive Number ◽  
Wuhan City ◽  
Epidemic Area ◽  
Stage 1 ◽  
The City

Objective To reconstruct the transmission trajectory of SARS-CoV-2 and analyze the effects of control measures in China. Methods Python 3.7.1 was used to write a SEIR class to model the epidemic procedure and proportional estimation method to estimate the initial true infected number. The epidemic area in China was divided into three parts, Wuhan city, Hubei province (except Wuhan) and China (except Hubei) based on the different transmission pattern. A testing capacity limitation factor for medical resources was imposed to model the number of infected but not quarantined individuals. Baidu migration data were used to assess the number of infected individuals who migrated from Wuhan to other areas. Results Basic reproduction number, R0, was 3.6 before the city was lockdown on Jan 23, 2020. The actual infected number the model predicted was 4508 in Wuhan before Jan 23, 2020. By January 22 2020, it was estimated that 1764 infected cases migrated from Wuhan to other cities in Hubei province. Effective reproductive number, R, gradually decreased from 3.6 (Wuhan), 3.4 (Hubei except Wuhan,) and 3.3 (China except Hubei) in stage 1 (from Dec 08, 2019 to Jan 22, 2020) to 0.67 (Wuhan), 0.59 (Hubei except Wuhan) and 0.63 (China except Hubei) respectively. Especially after January 23, 2020 when Wuhan City was closed, the infected number showed a turning point in Wuhan. By early April, there would be 42073 (95% confidence interval, 41673 to 42475), 21342 (95% confidence interval, 21057 to 21629) and 13384 (95% confidence interval, 13158 to 13612) infected cases in Wuhan, Hubei (except Wuhan) and China (except Hubei), respectively. Conclusion A series of control measures in China have effectively prevented the spread of COVID-19, and the epidemic should be under control in early April with very few new cases occasionally reported.

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